EOG-based eye movement recognition using GWO-NN optimization

Abstract

In recent times, the control of human-computer interface (HCI) systems is triggered by electrooculography (EOG) signals. Eye movements recognized based on the EOG signal pattern are utilized to govern the HCI system and do a specific job based on the type of eye movement. With the knowledge of various related examinations, this paper intends a novel model for eye movement recognition based on EOG signals by utilizing Grey Wolf Optimization (GWO) with neural network (NN). Here, the GWO is used to minimize the error function from the classifier. The performance of the proposed methodology was investigated by comparing the developed model with conventional methods. The results reveal the loftier performance of the adopted method with the error minimization analysis and recognition performance analysis in correspondence with varied performance measures such as accuracy, sensitivity, specificity, precision, false-positive rate (FPR), false-negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR) and the F1 score.

Analysis and synthesis of function data of human movement

“…Analogous to the experimental approach of the WARD data set, we used the K-CV method to establish a training sample and test sample library (the self-collected data includes the data of eight experimenters [12]; therefore, the value of K here is 8, which is the 8-fold cross-validation method); and finally, we used the SVM for classification and recognition, and the recognition rate reached 98.75%.…”